We propose a methodology for the
calculation of nanohardness by
atomistic simulations of nanoindentation. The methodology is enabled
by machine-learning interatomic potentials fitted on the fly to quantum-mechanical
calculations of local fragments of the large nanoindentation simulation.
We test our methodology by calculating nanohardness, as a function
of load and crystallographic orientation of the surface, of diamond,
AlN, SiC, BC2N, and Si and comparing it to the calibrated
values of the macro- and microhardness. The observed agreement between
the computational and experimental results from the literature provides
evidence that our method has sufficient predictive power to open up
the possibility of designing materials with exceptional hardness directly
from first principles. It will be especially valuable at the nanoscale
where the experimental measurements are difficult, while empirical
models fitted to macrohardness are, as a rule, inapplicable.
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